Customer Learning Notes

SkillDocs & knowledge

Turn customer conversation notes, interview transcripts, call summaries, CRM snippets, or research notes into shared team learning and next questions. Use when synthesizing raw customer notes, avoiding founder interpretation bottlenecks, extracting quotes and signals, updating beliefs, or deciding what to ask next.

Use Customer Learning Notes in Claude, ChatGPT or Ahel Desktop

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Also: Claude Code · Cursor · Codex

Then ask your AI: use the Customer Learning Notes skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Customer Learning NotesStart free

What this skill tells your AI

The instructions your AI receives, as published by hashgraph-online/awesome-codex-plugins in plugins/LVTD-LLC/skills/skills/customer-learning-notes/SKILL.md and read by Ahel’s review.

Use this skill after customer conversations to turn raw notes into team-readable evidence. Good notes make it harder to misremember, overfit, or let one founder become the sole source of customer truth.

Source Traceability

Primary source: The Mom Test by Rob Fitzpatrick, especially chapter 8 and the conclusion. Guidance is paraphrased for this MIT repo; authoring notes used converted EPUB lines 3613-4445.

Signal Taxonomy

Use these labels when synthesizing notes:

LabelMeaning
PainProblem, obstacle, annoyance, risk, or cost
GoalDesired outcome, job to be done, or priority
WorkaroundCurrent manual process, tool stack, hack, or substitute
MoneyBudget, cost, value, purchase process, or decision owner
PersonSpecific stakeholder, competitor, team, buyer, or intro lead
FeatureRequest, buying criterion, integration need, or implementation clue
EmotionStrong excitement, anger, embarrassment, fear, or skepticism
Follow-upPromise, task, intro, research item, or next step

Synthesis Workflow

  1. Preserve concrete facts separately from interpretation.
  2. Pull out short, useful quotes only when they are needed for traceability, positioning, or internal alignment.
  3. Tag signals using the taxonomy above.
  4. Group evidence by segment, problem, workaround, budget, and commitment.
  5. Identify contradictions and mixed-segment noise.
  6. Update beliefs, risks, and the next three questions.
  7. Recommend whether to continue, narrow the segment, ask for commitments, or move to building/testing.

Confidence Levels

LevelUse When
HighRepeated behavior from a focused segment, with concrete cost or commitment.
MediumSpecific evidence from a few good-fit conversations.
LowOne-off quotes, mixed segments, opinions, or weakly anchored claims.

Output Format

# Customer Learning Synthesis

## Source Notes
- Conversations:
- Segment:
- Date range:

## Evidence
| Signal | Evidence | Segment | Confidence | Implication |
|--------|----------|---------|------------|-------------|

## Belief Updates
- Stronger / weaker / new / rejected:

## Decisions
- Product, segment, positioning, sales or access:

## Next 3 Questions
1. [Question]
2. [Question]
3. [Question]

Workflow

Use workflows/synthesize-conversation-notes.md when the user provides raw notes, transcripts, call summaries, or interview excerpts.

Quality Bar

  • Do not summarize notes into vibes.
  • Do not let one loud quote outweigh repeated behavior from a focused segment.
  • Do not mix segments without labeling them.
  • Do not treat notes as useful until they have been reviewed and turned into updated beliefs or decisions.

Signals

GitHub stars
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Last commit
Oct 2026
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Item type
skill
Key
customer-learning-notes
Source
github.com/hashgraph-online/awesome-codex-plugins